Data Analysis 3: Pattern discovery and regression analysis

Term: 
Fall
Credits: 
2.0
Course Description: 

Type: core for MA in Economic Policy in Global Markets 1st yr, MSc in Business Analytics (full-time), MSc in Business Analytics (part-time)

elective for: PhD in Business Administration, MA in Global Economic Relations (full-time), MA in Global Economic Relations (part-time), MSc in Finance (full-time), MSc in Finance (part-time)

Seminars:

  • 1.) STATA: Mandatorily elective for MA in Economic Policy in Global Markets  elective for MA in Global Economic Relations Full Time & Part Time  and PhD in Business Administration
  • 2.) R- Full Time: Mandatorily elective for MA in Economic Policy in Global Markets and MSc in Business Analytics Full Time & Part Time students, elective for  PhD in Business Administration and MSc in Finance Full Time students
  • 3.) R- Part Time: core for MSc in Business Analytics Part Time& Full Time students, elective for MA in Global Economic Relations Part- Time and MSc in Finance Part-Time students

Uncovering patterns in the data can be an important goal in itself, and it is the prerequisite to
establishing cause and effect and carrying out predictions. The course starts with simple regression
analysis, the method that compares expected y for different values of x to learn the patterns of
association between the two variables. It discusses nonparametric regressions and focuses on the linear
regression. It builds on simple linear regression and goes on to enriching it with nonlinear functional
forms, generalizing from a particular dataset to other data it represents, adding more explanatory
variables, etc. We also cover regression analysis for time series data, binary dependent variables, as well
as nonlinear models such as logit and probit.

Timing: Full time, part time: Friday afternoon

 

Uncovering patterns in the data can be an important goal in itself, and it is the prerequisite to establishing cause and effect and carrying out predictions. The course starts with simple regression analysis, the method that compares expected y for different values of x to learn the patterns of association between the two variables. It discusses nonparametric regressions and focuses on the linear regression. It builds on simple linear regression and goes on to enriching it with nonlinear functional forms, generalizing from a particular dataset to other data it represents, adding more explanatory variables, etc. We also cover regression analysis for time series data, binary dependent variables, as well as nonlinear models such as logit and probit.

Learning Outcomes: 

By successfully completing the course the students will be able to:
- Successfully formulate research questions that are answerable by empirical analysis;
- Produce meaningful descriptive statistics and informative graphs;
- Carry out simple regression analysis;
- Discuss and interpret results, understand validity and constraints.
- Present empirical analysis and write short reports with data;

Assessment: 

- Start-of-the-class Quizzes (10%)
- Assignments (40%)
- Closed book exam (50%)

Prerequisites: 

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